predictive AI - logistics - process optimization - cross-docking
One of our logistics customers uses a crossdocking process to efficiently move goods between incoming and outgoing shipments, minimising storage time. We have developed a progressive web app to help them digitise this process, but decisions on goods movement and placement are still manually decided and suboptimal.
Implementing AI could optimize this by predicting the best routes and locations for goods within the crossdock based on data such as arrival times, destinations, and priorities. AI would help minimize travel distance, balance workload, reduce congestion in busy zones, and reduce trailer dwell time. Next to this it could allow for dynamic adjustments in real-time, responding to delays or changes in volume, and improving space allocation, resource usage, and throughput. This would lead to faster processing, reduced costs, and more reliable deliveries.
The developed app is already integrated with the customer’s Transport Management System (TMS). Therefore we have access to both data upon historic and real-time incoming and outgoing transport movements and orders etcetera, as well as the data that is generated within the crossdock itself on for example the loading state of trucks or the placement of goods.
The goal of this graduation assignment is to create an AI-based solution to optimize the crossdock using data from various sources, such as the customer’s Transport Management System. This data can include both historical and live information about shipments, order priorities, truck loads, and how goods are stored in the crossdock.
With this detailed data, the aim is to build (a) smart model(s) that can predict shipment flows and suggest or automate the best ways to move and place goods. This should help reduce travel distances, spread the workload more evenly, prevent congestion, and lower trailer waiting times.
The final result should be a practical tool that supports or automates decision-making in the crossdock, making operations faster, more efficient, and less costly. You will work together with logistics and IT staff to make sure the solution works well in real life and can be easily implemented in the current process. The end product should be a tested AI solution with proven improvements and a clear plan for putting it into use, which can be within the existing app.
Get in touch with Martijn, founder of Bullit.
+31 6 39 56 09 34